Self-training Algorithm Combining Density Peak and Integrated Filter
Accurately selecting high confidence samples is the key to improve the classification performance of self-training algo-rithm.A self-training algorithm combining density peaks and integrated filters was proposed to address misclassified samples in self-training iteration process.The algorithm first used density peak clustering to calculate the density and peak value of samples,and constructed an initial high confidence sample set.Secondly,in order to filter out misclassified samples in self-training iteration process,a novel integrated filter was designed.High confidence samples were further selected from the initial high confidence sample set and added to the labeled sample set for iterative training.Comparative experiments were conducted with 4 related self-training algorithms on 9 datasets.The experimental results show that the average accuracy and F-score of the proposed algorithm are 67.90％and 65.54％respectively,and its classification performance is significantly superior to that of the comparison algorithm.
万方数据
维普
